基於注意力機制長短期記憶深度學習 之機器剩餘可用壽命預估;Attention-based Long Short-Term Memory Deep Learning for Estimating Machinery Remaining Useful Life

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Title:	基於注意力機制長短期記憶深度學習之機器剩餘可用壽命預估;Attention-based Long Short-Term Memory Deep Learning for Estimating Machinery Remaining Useful Life
Authors:	陳韋儒;Chen, Wei-Ru
Contributors:	資訊工程學系
Keywords:	智慧工廠;剩餘可用壽命;深度學習;遞歸神經網路;長短期記憶;注意力機制;Smart Factory;Remaining Useful Life;Deep Learning;Recurrent Neural Network;Long Short Term Memory;Attention-based Mechanism
Date:	2018-06-08
Issue Date:	2018-08-31 14:45:37 (UTC+8)
Publisher:	國立中央大學
Abstract:	受到德國工業4.0概念的影響，各大製造業為了保有競爭力，紛紛往「智慧化」生產的腳步邁進。利用產線的互聯網化，收集大量數據，再透過數據分析，達到自動調整生產流程、能源管理智慧化、預測需求以降低庫存及預測機械故障等目標、進而以最有效率的方式製造彈性乃至即時的客製化產品。本篇論文著重於預測機器剩餘可用壽命(Remaining Useful Life, RUL)，屬於機器預診斷的一環，是一種新的維運策略思維，透過生產製造過程中所產生的巨量資料進行分析，再進行分析預測，以利提前替換或維修，避免設備在運作的過程中突然停止，導致生命或財產的損失。本篇論文利用遞歸神經網路(Recurrent Neural Network, RNN)深度學習(Deep Learning)方法，預估機器的剩餘可用壽命。並利用長短期記憶(Long Short-Term Memory, LSTM)模型，再加入基於注意力機制，對特別導致損壞的因子進行加權，使其更能萃取時間序列資料的特徵，達到精確預測機器的剩餘可用壽命。我們以NASA所提供的C-MAPSS(Commercial Modular Aero-Propulsion System Simulation)資料集為實驗案例，以所提的方法預估飛機渦輪引擎的剩餘壽命，並以參考文獻中的各種方法如MLP、SVR、RVR和CNN、Stack LSTM為比較對象。實驗顯示，在均方根差(Root Mean Squared Error, RMSE)或是資料集本身定義的Scoring Function的評分準則下，所提的方法有最佳的預測能力。 ;Influenced by the revolutionary concept of German Industry 4.0, major manufacturing industries have been moving from automatic production into smart production for maintaining their competitiveness. Industry 4.0 advocates smart factories that use Internet-enabled assembly lines to collect large amounts of data and then through data analysis to achieve the goals of smartly adjusting production processes, intelligently saving energy, precisely forecasting customer demands, and accurately predicting mechanical failures. In general, smart factories can yield flexible and even customized products in the most efficient way. This paper focuses on estimating machine remaining useful life (RUL), which is a kind of the machine condition pre-diagnosis. By accurate RUL estimation, we can perform predictive maintenance, instead of preventive maintenance, to avoid sudden breakdown of machines/components during the operation process to prevent huge loss. This paper proposes a Recurrent Neural Network (RNN) deep learning method to estimate the remaining useful life of machines, especially the aero-propulsion engines. The proposed method uses the Long Short-Term Memory (LSTM) model with the attention-based (AB) mechanism. The LSTM model is useful for extracting relationship between time-series data items that are far separated, and the AB mechanism can help emphasize different factors that affect the RUL in different time. The NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is taken to evaluate the URL estimation accuracy of the propose method. The evaluated results are compared with those of related methods, namely the MLP, SVR, RVR, CNN, Stack LSTM methods. Comparisons show that the proposed method is superior to the others in terms of the scoring function value defined by the C-MAPSS dataset, and the Root Mean Squared Error (RMSE) .
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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